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This template will perform RAG using Astra DB (AstraDB vector store class)

Environment Setup​

An Astra DB database is required; free tier is fine.

  • You need the database API endpoint (such as ...
  • ... and a token (AstraCS:...).

Also, an OpenAI API Key is required. Note that out-of-the-box this demo supports OpenAI only, unless you tinker with the code.

Provide the connection parameters and secrets through environment variables. Please refer to .env.template for the variable names.


To use this package, you should first have the LangChain CLI installed:

pip install -U "langchain-cli[serve]"

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-astradb

If you want to add this to an existing project, you can just run:

langchain app add rag-astradb

And add the following code to your file:

from astradb_entomology_rag import chain as astradb_entomology_rag_chain

add_routes(app, astradb_entomology_rag_chain, path="/rag-astradb")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-astradb")


Stand-alone repo with LangServe chain: here.

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